Alterations in osteocyte mediated osteoclastogenesis during estrogen deficiency and under ROCK-II inhibition: An in vitro study using a novel postmenopausal multicellular niche model
Bibliographic record
Abstract
This study sought to derive an enhanced understanding of the complex intracellular interactions that drive bone loss in postmenopausal osteoporosis. We applied an in-vitro multicellular niche to recapitulate cell-cell signalling between osteocytes, osteoblasts and osteoclasts to investigate (1) how estrogen-deficient and mechanically loaded osteocytes regulate osteoclastogenesis and (2) whether ROCK-II inhibition affects these mechanobiological responses. We report that mechanically stimulated and estrogen-deficient osteocytes upregulated RANKL/OPG and M-CSF gene expression, when compared to those treated with 10 nM estradiol. Osteoclast precursors (RAW 264.7) cultured within this niche underwent significant reduction in osteoclastogenic gene expression (CTSK), and there was an increasing trend in the area covered by TRAP+ osteoclasts (24% vs. 19.4%, p = 0.06). Most interestingly, upon treatment with the ROCK-II inhibitor, RANKL/OPG and M-CSF gene expression by estrogen-deficient osteocytes were downregulated. Yet, this inhibition of the pro-osteoclastogenic factors by osteocytes did not ultimately reduce the differentiation of osteoclast precursors. Indeed, TRAP and CTSK gene expressions in osteoclast precursors were upregulated, and there was an increased trend for osteoclast area (30.4% vs. 24%, p = 0.07), which may have been influenced by static osteoblasts (MC3T3-E1) that were included in the niche. We conclude that ROCK-II inhibition can attenuate bone loss driven by osteocytes during estrogen deficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".